Drew Purves

I am a research scientist at DeepMind, where I am drawing on my
experience in computational ecology, simulation modelling, machine learning
and software development to help create Artificial General Intelligence.

My interest in computers began in Christmas 1984 when, at age seven, I was lucky
enough to receive the Commodore 64 that sits in my office at home today.
Although the games that you could buy on cassette were fun, I preferred to
program my own in BASIC. Worm-but-in-a-maze. Tetris-but-top-down. Sokoban-but-I-programmed-it-myself.
Funnily enough my friends and family never loved these games as much as I did :)

My interest in ecology began later on when a sharp-eyed biology teacher, Richard Hepell,
noticed the Artificial Life simulations I was running on the biology
department’s 386s. Richard handed me a copy of the standard ecology
undergraduate textbook (‘Ecology’ by Begon, Harper and Townsend), opening my
eyes to the fascinating dynamics of ecosystems, and our attempts to understand
them through theory. For this reason I applied to study natural sciences, with
a focus on all things evolutionary and ecological, at Cambridge University.
Nobody in my family had ever been to university, so I had no idea about how to
navigate what was, genuinely, a whole new world for someone with my
background. But I figured that, surely, it would help if I turned up at the
interview with some research results? So I brought along some charts showing
punctuated equilibrium in simulations of the evolution of foraging strategies.
Looking back now, I think this probably did help me to get in ;)

Studying ecology at Cambridge was a privilege, which excellent teaching that
was, crucially, combined with many field trips to woodlands, fens, the coast,
islands, and even to a the tiny village in Andalucia which has since become a
spiritual second home for my family. These trips reinforced how interesting
nature was, but also reinforced how far we were from having the kind of
rigorous, mathematical understanding of ecosystem that we have long taken for
granted in other areas. So I opted to my PhD with a leading theoretical
ecologist, Richard Law, at the University of York, who was one of the people
trying to create a firm mathematical and computational foundation for ecology.
During that PhD I learned to go beyond just creating interesting simulations,
and instead use them rigorously to address scientific questions. I also
learned a little about how to constrain ecological models against data, in
order to create simulations that could actually capture real, observed
dynamics -- something that still gives me a buzz to this day.

Next, I was lucky enough to land a five-year postdoc at Princeton University,
with another leading ecologist, Steve Pacala. Steve’s group was at the very
cutting edge in terms of constraining ecological models against data. During
that postdoc I learned about that magical thing The Likelihood – suddenly, stats made sense! –
and Bayesian approaches. I ended up developing my own adaptive MCMC algorithm
(now available under the name Filzbach) which, over the following years,
enabled myself and others to fit all kinds of really nasty models to all kinds of data, both at
Princeton and later on at Microsoft Research. I focussed in particular on
forests, which are an amazing type of ecosystem to study for many reasons, not
least the amazing amount of public data available. In the years
2001-2006, I was routinely fitting highly non-linear simulation models to over
a million measurements of real trees. On a more general level, I also learned
that problems that were highly important from a societal perspective, like
climate change, could also be intellectually and scientifically challenging.
Obvious in retrospect, but novel at the time.

Just when it was starting to look like I might land a permanent job in
academia, a truly unique opportunity arose at Microsoft Research, which wanted
to build a Computational Ecology and Environmental Science group (CEES), under
Stephen Emmott and Rich Williams. Strangely, they wanted to do this at their
centre in Cambridge, UK. It was a no-brainer. Thanks in large part to a strong
steer from Stephen and Rich, for the next eight years myself and the other members
of the team pursued a two-part mission to carry out, and publish, research
that pushed the boundaries of computational ecology; whilst creating new
software tools to help others do the same. I became head of the group early
on, when Rich moved on to pursue other challenges. Our team never grew above
ten full-time staff (permanent scientists, postdocs, developers), but
nonetheless we published well over 200 research papers (including at least five
in Science and Nature), many of which represented significant advances in the
field (again, see below). We also generated exciting early data science software prototypes that
played significant roles within Microsoft as a whole. Looking back though, the
thing that I am most proud of about CEES is that the CEES family – from the
permanent scientists, to the developers, to the postdocs and PhD students that
we mentored, to the many interns that we hosted – have landed amazing,
exciting jobs in academia, NGOs, businesses, and government which are clearly
related to their experiences within the group. On a daily basis they are
drawing on that experience to address everything from forest ecology, to
remote sensing, to biology and medicine, to conservation, to the internet of
things, to data visualization, to national security, to applications of
machine learning in the wider economy. This is the true legacy of
CEES, and it is thrilling to watch it play out.

As for me, I recently found myself faced with another no-brainer – an invite from Demis Hassabis to
help Google DeepMind create human-level Artificial General Intelligence. How could anyone turn down the chance
to be involved in the most important technological transition in human
history? But also, this kind of AI
is needed to address major global challenges, as I know from
personal experience thinking about them for many years (these
problems are so complex that is not obvious we can solve them without
Artificial General Intelligence). A year or more before I joined DeepMind I had realized
that it was the most exciting company on the planet. What I didn’t know was
that I might have a role to play within it: remember, I don't have a background in
neuroscience, reinforcement learning, or deep learning! Luckily, it turned out
there was a role. Indeed, a surprisingly ambitious and long-term one that
will require me to draw on all my experience so far, and to
learn about and move into several fields, all of which are new to me, many of which are new to the
world.

Please note the mixture of luck, encouragement and patronage that has shaped my career to date.
I have always tried to pass on some of the latter two wherever possible, for example by taking up external
positions as Treasurer of the British Ecological Society, Affiliated Lecturer at Cambridge University, and
Honorary Reader at UCL, where I help to mentor two PhD students.